Fusemachines is hiring a Senior Machine Learning Engineer to architect, build, and deploy high-performance machine learning systems that power our technology stack. You’ll work across the entire ML lifecycle—from processing massive volumes of data to developing and deploying low-latency models. This role requires a strong hybrid skill set: deep expertise in applied machine learning combined with production-grade software engineering skills, supporting our mission of democratizing AI by providing high-quality AI education in underserved communities and helping organizations achieve their full potential.
What You'll Do
- Process and extract features from massive, highly sparse datasets (terabytes/petabytes) using SQL, Python, and distributed computing frameworks.
- Architect offline and online feature pipelines, managing real-time feature computation and low-latency feature stores to ensure zero online/offline skew.
- Perform rigorous missingness analysis, leakage checks, and safely handle high-cardinality categorical variables.
- Train, tune, and scale supervised learning models using advanced gradient boosting (XGBoost, LightGBM, CatBoost) and Factorization Machines.
- Design and implement Deep Learning architectures for structured/recommendation data using PyTorch or TensorFlow.
- Apply rigorous tabular modeling practices including meticulous leakage prevention, class imbalance strategies, and robust cross-validation on time-split data.
- Write clean, object-oriented, modular production code and transition models from research to high-performance serving environments using tools like ONNX and TensorRT.
- Design and maintain robust MLOps pipelines for automated model retraining, versioning, shadow deployments, and CI/CD.
- Monitor production models for data drift, concept drift, and performance degradation in real-time, implementing automated alerting and fallback mechanisms.
- Design rigorous A/B and multivariate experiments to measure model impact and drive business decisions.
What We're Looking For
- Proven experience scaling data engineering and feature pipelines for massive, sparse datasets.
- Expertise in training, tuning, and deploying supervised learning models and deep learning architectures.
- Strong production software engineering skills with the ability to write clean, modular, and object-oriented code.
- Deep hands-on experience with the full machine learning lifecycle and MLOps practices.
- Excellent analytical skills for model evaluation, experimentation, and monitoring.
Technical Stack
- Languages & Querying: SQL, Python
- Distributed Computing: Spark, Ray
- ML Frameworks: XGBoost, LightGBM, CatBoost, PyTorch, TensorFlow
- Productionization: ONNX, TensorRT
Work Mode
This is a remote position.
Fusemachines is an equal opportunity employer.
